Page 97 - The Real Work Of Data Science Turning Data Into Information, Better Decisions, And Stronger Organizations by Ron S. Kenett, Thomas C. Redman (z-lib.org)_Neat

Epilogue 89

bear, and present their results in simple, powerful ways. We introduced practical statistical
efficiency as a means for data scientists to assess and improve their impact.
We took a hard look at what it means to be data‐driven (Chapter 10) and explored dealing
with bias in decision‐making (Chapter 11). We urged data scientists to raise everyone’s
game by teaching their colleagues and decision‐makers to become more educated,
demanding customers of data science (Chapters 12 and 13) and CAOs to help senior leaders
understand the complexities of the data landscape (Chapter 14). We urged CAOs to find the
right organizational homes for data science (Chapter 15), and we explored the analytic maturity
ladder (Chapter 16) as a means to mark progress and improve still further.
All this to help today’s data scientist become more effective today.
We’re even more interested in the not‐too‐distant future. Data and data science can be a
force for transformational good in all aspects of human endeavor, from making us all freer and
safer, to promoting equality, to better health care at lower cost, and to economic growth and
shared prosperity. But it would be naïve, even reckless, to assume this will happen on its own.
Data and data science are completely agnostic – data doesn’t care whether it is simply wrong,
and algorithms don’t give a whit whether they invade privacy or promote social injustice. It is
time for data scientists and CAOs to step up to their real work.